Unsupervised semantic annotation of Web service datatypes

Chifu, Emil St.; Letia, Ioan Alfred

doi:10.1109/iccp.2010.5606464

Cited by 7 publications

(10 citation statements)

References 4 publications

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“…So they just need to discover the relevant ontology and then leverage it for annotating WSDL elements using ontology mapping techniques. Chifu et al [5] proposed an supervised classification (based on neural network) to classify WSDL data types where the classes are corresponding to respective concepts in the given taxonomy. From a different perspective, Liu and Liu [13] targeted learning of service ontology which focuses on service level ontology rather than data type semantics.…”

Section: Related Workmentioning

confidence: 99%

Ontology acquisition from web service descriptions

Mokarizadeh

Küngas

Matskin

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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The lack of formally expressed semantics in web services complemented with the increasing number of available web services is the main obstacle in analyzing and using the existing web services exposed in the Web. In the absence of appropriate reference domain ontology, annotation of existing web services is dependent on ontology development and ontology learning techniques. In this paper we present an unsupervised ontology learning approach tailored to learning from WSDL documents. The most specific feature of the suggested approach is that it constructs (semi-) automatically ontology fragments from a collection of WSDL documents, that lack any extra textual documentation, by just exploiting element names in the WSDL document. The suggested approach combines both linguistic and statistic analysis techniques such as lexico-syntactic patterns and term co-occurrence analysis. The preliminary results show that the generated ontology captures correctly more than half of the semantic classes and instances as well as taxonomic and non-taxonomic relations, hence, providing a reasonable basis for automatic web services annotation.

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Section: Related Workmentioning

confidence: 99%

Ontology acquisition from web service descriptions

Mokarizadeh

Küngas

Matskin

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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“…Raw data were classified through an ANN and mapped to ontology classes for performing rule-based reasoning. In [6], an unsupervised model was used for classifying Web Service datatypes in a large number of ontology classes, by adopting an extended ANN. Also in this case, however, mining was exploited only to map data to a single class.…”

Section: Related Workmentioning

confidence: 99%

Machine learning in the Internet of Things: A semantic-enhanced approach

Ruta

Scioscia

Loseto

et al. 2018

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New Internet of Things (IoT) applications and services more and more rely on an intelligent understanding of the environment from data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking meaningful representations and explanations of taxonomies. This paper proposes a framework for a semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection by treating the typical classification problem of ML as a resource discovery. Outputs of classification are endowed with machine-understandable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.

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“…Since there is an abundance of Web Services on the Internet, many studies concentrate on the Web service annotation, such as [6][7][8][9][10][11][12][13]. In [6][7][8][9][10], XML schema is annotated.…”

Section: A Semantic Annotationmentioning

confidence: 99%

“…In [6][7][8][9][10], XML schema is annotated. In [11], operations, message parts, XML schema are annotated.…”

Section: A Semantic Annotationmentioning

confidence: 99%

“…In [13], interfaces, operations, messages and XML schema are annotated. Though different approaches are proposed in [6][7][8][9][10][11][12][13], the basic idea is describing the implicit semantic information in terms of concepts provided by the ontology. These studies are good at transforming Web services into Semantic Web services, however, there is no work that gives an answer to the question that how much implicit semantic information can be described.…”

Section: A Semantic Annotationmentioning

confidence: 99%

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Analyzing Distribution of Implicit Semantic Information in Web Services

Feng

Chen

2013

2013 IEEE 37th Annual Computer Software and Applications Conference Workshops

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Semantic annotation for Web services represents an important approach that transforms Web services into Semantic Web services. This paper analyzes the distribution of implicit semantic information to enhance the understanding about Web services. After quantifying implicit semantic information in Web services, the PCA methodology is utilized for obtaining principal components of implicit semantic information. The experiment conducted on 11836 WSDL documentations shows that almost 97 percent of implicit semantic information is distributed in inputs and outputs. Thus, the evaluation of how much implicit semantic information can be described by semantic annotation is offered.

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Unsupervised semantic annotation of Web service datatypes

Cited by 7 publications

References 4 publications

Ontology acquisition from web service descriptions

Ontology acquisition from web service descriptions

Machine learning in the Internet of Things: A semantic-enhanced approach

Analyzing Distribution of Implicit Semantic Information in Web Services

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